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Advances in Civil Engineering
Volume 2010 (2010), Article ID 749578, 20 pages
Research Article

Computer Simulation of Stochastic Wind Velocity Fields for Structural Response Analysis: Comparisons and Applications

1Department of Civil and Environmental Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy
2Department of Structural and Geotechnical Engineering, University of Rome “La Sapienza”, Via Eudossiana 18, 00184 Rome, Italy

Received 14 October 2009; Revised 1 April 2010; Accepted 13 April 2010

Academic Editor: Chungxiang Li

Copyright © 2010 Filippo Ubertini and Fabio Giuliano. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The digital simulation of wind velocity fields, modeled as multivariate stationary Gaussian processes, is a widely adopted tool to generate the external input for response analysis of wind-sensitive nonlinear structures. The problem does not entail any theoretical difficulty, existing already a large number of well-established techniques, such as the accurate weighted amplitude wave superposition (WAWS) method. However, reducing the computational effort required by the WAWS method is sometimes necessary, especially when dealing with complex structures and high-dimensional simulation domains. In these cases, approximate formulas must be adopted, which however require an appropriate tuning of some fundamental parameters in such a way to achieve an acceptable level of accuracy if compared to that obtained using the WAWS method. Among the different techniques available for this purpose, autoregressive (AR) filters and algorithms exploiting the proper orthogonal decomposition (POD) of the spectral matrix deserve a special attention. In this paper, a properly organized way for implementing stochastic wind simulation algorithms is outlined at first. Then, taking the WAWS method as a reference from the viewpoint of the accuracy of the simulated samples, a comparative study between POD-based and AR techniques is proposed, with a particular attention to computational effort and memory requirements.